Automated Assessment of Fruit Quality Using Transfer Learning and MobileNetV2
Year: 2025
Type: article
Abstract: Fruit quality and health strongly affect market value, customer satisfaction, and overall farm yield. Manual inspection methods remain exceedingly labor-intensive. They are fairly subjective and vulnerable to inconsistencies. This paper presents an automatic system that uses many advanced deep learning methods, specifically transfer learning via MobileNetV2, to categorize fruits by freshness or spoilage. Classical machine learning(ML) methods like Support Vector Machines, Random Forest, and K-Nearest Neighbors are comparatively analyzed with modern Convolutional Neural Network (CNN) designs (like VGG16, MobileNetV1, and MobileNetV2). Many experimental evaluations reveal that MobileNetV2 not only provides substantially outstanding training accuracy but maintains consistently strong validation metrics. These findings underscore the model's potential for deployment on mobile platforms as well as in real-time agricultural applications, thereby leading to better productivity along with reduced post-harvest losses.
Authors: Odra Bira, Sreeja Sunkeswaram, Naresh R
Institutions: CMR University, Indian Institute of Technology Madras
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: diamond
OpenAlex ID: W4409166937